This tutorial demonstrates how to manually set up a workflow for loading, embedding, and storing documents using GPT4All and Chroma DB, without the need for Langchain.
Ensure you have the following packages installed:
pip install gpt4all chromadb requests beautifulsoup4
We start by fetching a document from a web source and splitting it into smaller chunks for easier processing.
import requests
from bs4 import BeautifulSoup
def fetch_document(url):
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
return soup.get_text()
def split_text(text, chunk_size=500):
return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
# Example usage
url = "https://example.com/document"
document_text = fetch_document(url)
chunks = split_text(document_text)
Now, we embed each text chunk using GPT4All.
# Hypothetical example for GPT4All embedding
from gpt4all import GPT4AllEmbedding
embedder = GPT4AllEmbedding()
embedded_chunks = [embedder.embed(chunk) for chunk in chunks]
We then store these embeddings in a Chroma DB instance for later retrieval.
from chromadb import ChromaDB
db = ChromaDB("path_to_your_database")
for i, embedding in enumerate(embedded_chunks):
db.store(embedding, document_id=i)
Finally, implement a function for similarity search within the stored embeddings.
def search_similar_documents(query, embedder, db, top_n=5):
query_embedding = embedder.embed(query)
similar_docs = db.search(query_embedding, top_n=top_n)
return similar_docs
# Example usage
query = "Your search query"
similar_documents = search_similar_documents(query, embedder, db)
This approach allows for a flexible and manual control over the entire document embedding and retrieval process, making it ideal for custom implementations not reliant on Langchain.
Consider it done :)
I’ve outlined a hypothetical step by step on it and added it as a markdown file to the gist.